An IoMT-based Federated Learning Survey in Smart Transportation

被引：0
作者：
Karnam G.V. [1 ]
Maddikunta P.K.R. [1 ]
机构：
[1] School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Tamil Nadu, Vellore
来源：
Recent Advances in Computer Science and Communications | / 4卷 / 20-39期
关键词：
Deep learning; dual attention-based federated learning; explainable artificial intelligence; intelligent transport system; machine learning; road side unit;
D O I：
10.2174/0126662558286756231206062720
中图分类号：
学科分类号：
摘要：
Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency response and reducing the impact of accidents on victims. Smart Transportation uses this technology to improve the efficiency and safety of transportation systems. The current Artificial Intelligence applications lack transparency and interpretability which is of utmost importance in critical transportation scenarios, such as autonomous vehicles, air traffic control systems, and traffic management systems. Explainable Artificial Intelligence (XAI) provides a clear, transparent explanation and actions. Traditional Machine Learning techniques have enabled Intelligent Transportation systems by performing centralized vehicular data training at the server where data sharing is needed, thus introducing privacy issues. To reduce transmission overhead and achieve privacy, a collaborative and distributed machine learning approach called Federated Learning (FL) is used. Here only model updates are transmitted instead of the entire dataset. This paper provides a comprehensive survey on the prediction of traffic using Machine Learning, Deep Learning, and FL. Among these, FL can predict traffic accurately without compromising privacy. We first present the overview of XAI and FL in the introduction. Then, we discuss the basic concepts of FL and its related work, the FL-IoMT framework, and motivations for using FL in transportation. Subsequently, we discuss the applications of using FL in transportation and open-source projects. Finally, we highlight several research challenges and their possible directions in FL. © 2024 Bentham Science Publishers.
引用
收藏
页码：20 / 39
页数：19
相关论文
共 111 条
[1]  
Saeed W., Omlin C., Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities, Knowl. Base. Syst, 263, (2023)
[2]  
Omolara A.E., Alabdulatif A., Abiodun O.I., Alawida M., Alabdulatif A., Alshoura W.H., Arshad H., The internet of things security: A survey encompassing unexplored areas and new insights, Comput. Secur, 112, (2022)
[3]  
Qu Y., Pokhrel S.R., Garg S., Gao L., Xiang Y., A block chained federated learning framework for cognitive computing in industry 4.0 networks, IEEE Trans. Industr. Inform, 17, 4, pp. 2964-2973, (2021)
[4]  
Konecn y J., McMahan H. B., Yu F. X., Richtarik P., Suresh A. T., Bacon D., Federated learning: Strategies for improving communication efficiency, (2016)
[5]  
Lv Y., Duan Y., Kang W., Li Z., Wang F.Y., Traffic flow prediction with big data: A deep learning approach, IEEE Trans. Intell. Transp. Syst, 16, 2, pp. 1-9, (2014)
[6]  
Du Z., Wu C., Yoshinaga T., Yau K.A., Ji Y., Li J., Federated learning for vehicular internet of things: Recent advances and open issues, IEEE Comput. Graph. Appl, 1, pp. 45-61, (2020)
[7]  
Yang Q., Liu Y., Chen T., Tong Y., Federated machine learning: Concept and applications, ACM Trans. Intell. Syst. Technol, 10, 2, pp. 1-19, (2019)
[8]  
Li Q., Wen Z., Wu Z., Hu S., Wang N., Li Y., Liu X., He B., A survey on federated learning systems: Vision, hype and reality for data privacy and protection, IEEE Trans. Knowl. Data Eng, (2021)
[9]  
Park J., Samarakoon S., Elgabli A., Kim J., Bennis M., Kim S.L., Debbah M., Communication-efficient and distributed learning over wireless networks: Principles and applications, Proc. IEEE, 109, 5, pp. 796-819, (2021)
[10]  
Aledhari M., Razzak R., Parizi R. M., Saeed F., Federated learning: A survey on enabling technologies, protocols, and applications, IEEE Access, 8, pp. 140699-140725, (2020)
12345678910